Speech recognition is the conversation of sound to text. It is the process of understanding what the sounds mean, and while it is something that the human brain of course does quite well, computers tend to be less accurate at this task. Advancements in having computers do the same and have continued to proceed from the days when one had to “train” the computer to understand a specific voice, to be able to determine what is being said based on the context of the sentence or how the user corrects the resulting text, in order to “automatically” learn the voice of the speaker.
Still, speech to text is not always accurate, and there is much room for improvement. U.S. Pat. No. 6,754,629 to Qi, for example, discloses choosing from available speech recognition engines, because any given engine is not always accurate. Baruch et al, in U.S. Pat. No. 7,203,651 discloses similarly. For different tasks, different vocabulary, different background noise conditions, and the like, one person may choose one engine rather than another.
By making the choice as to which speech engine recognition engine to use, one can achieve greater success with less frustration and correction needed. These common drawbacks of speech recognition can be lessened, but the question remains—how does one achieve this? Thus, there is a need in the prior art to improve upon the accuracy of speech recognition in order to increase productivity.